Recently there has been an increased awareness that machine learning can be used in the prediction of adverse medical outcomes. The accuracy of such predictions made by computers using machine learning is predicated on the computer's ability to extract the right information from the data. This extraction is termed feature extraction, feature construction or feature engineering.
The ability to extract the useful features in cardiac events is an important problem because millions of acute coronary events occur each year in the United States alone, resulting in the death of 1 out of 6 deaths in the US. Further, 8-19% of those Americans who had a heart attack will die within 12 months of discharge from the hospital. If physicians could accurately identify high-risk patients, i.e. stratify the risk of death, it may be possible to improve the matching of patients to therapy and thereby potentially improve outcomes. One way this might be accomplished is by the analysis of electrocardiograms (ECG) according to their various characteristics.
An ECG is a substantially repeating pattern that measures the electrical activity of the heart. The ECG is only quasi-periodic due to natural variations in heart rate. This variation in heart rate is a characteristic that may be analyzed and is termed Heart Rate Variability (HRV). One example of HRV is the standard deviation of all “normal” heart beat intervals, termed (HRV-SDNN).
A second characteristic that may be analyzed is the morphological variability (MV) in the ECG signal. MV measures the beat to beat variability in the shape of the beats in a patient's long term ECG signal. MV is determined by obtaining an ECG, and, after preprocessing to clean the signal, segmenting the signal into a time series of beats. For each pair of beats, the differences in beat to beat morphology are measured as morphological distances (MD) using dynamic time warping. (See U.S. Pat. No. 8,346,349, the entire contents of which are herein incorporated by reference in their entirely.) The series of inter-beat distances is termed the MD time series. The MD time series is then divided into fixed time intervals or window segments and the power spectral density of each window then determined. The power spectral density in a 0.30-0.55 Hz frequency band, termed a diagnostic band, in each window is then measured. The 90th percentile of the spectral energies of the diagnostic band in all the windows is the Morphologic Variability (MV) of the ECG. Morphological variability may be used as an indicator that unless treated, the patient is at a higher risk of dying within a predetermined period.
Thus there exist several frequency domain analyses, in which periodic changes are measured. Much work has been performed in frequency domain analyses of ECG signals. However, in the frequency domain, the quasi-periodicity of the ECG introduces significant problem. For example assume that there are two patients with constant heart rates of 60 and 120 beats-per-minute respectively. A frequency domain of 0.5 Hz (sampled every 2 seconds) corresponds to every 2 beats in the first patient but every 4 beats in the second. However in a beat-frequency domain where the notion of frequency is expressed with respect to heartbeats rather than time, every 2 beats corresponds to 0.5 Hz in the first patient but 1.0 Hz in the second. Thus the frequency bands measured in “time-space” and “beat-space” differ. Which frequency domain should be used to analyze the ECG depends on whether the phenomenon of interest is expected to be periodic with respect to time, or periodic with respect to heartbeats. The choice of the “wrong” type of frequency domain may result in ambiguous observations when viewed across patients and when viewed across time for any given patient.
The present invention addresses this issue.